scholarly journals Machine Learning Based Nutrient Deficiency Detection in Crops

2020 ◽  
Vol 8 (6) ◽  
pp. 5330-5333
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
Crop Yield ◽  
Crop Production ◽  
Labour Force ◽  
Nutrient Deficiency ◽  
Input Image ◽  
Robotic Vehicle

Indian economy is mainly based on Agriculture, involves the process of cultivating certain plants for producing food and many other desired products and raising of domesticated animals. Nutrients play a major role in agriculture and crop production. There are number of reasons for decreasing of crop yield. One such factor involved is nutrient deficiency. The proper detection of nutrient deficiency and appropriate fertilizer for that deficiency are the major problems faced by many farmers. Hence, in order to improve productivity, Automation in agriculture evolved drastically in recent years. This paper aims at designing an automatic robotic vehicle which detects the nutrient deficiency in crops just by simply capturing the image of leaves of the crop plants. The captured image is then processed by using the convolutional neural networks (CNN). This technique uses captured image, processing it by comparing it with the already available dataset. When the input image is matched or partially matched with any one of the existing images in the dataset, it will provide the result of nutrient deficiency in crops, in terms of the percentage. The name of disease associated with nutrient deficiency and appropriate amount of fertilizer will be displayed in the LCD. This will reduce the problems of the labour force and the burden of farmers.

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

A versatile approach based on convolutional neural networks for early identification of diseases in tomato plants

2021 ◽  
Author(s):  
N. V. Megha Chandra ◽  
K. Ashish Reddy ◽  
G. Sushanth ◽  
S. Sujatha
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
Crop Yield ◽  
Tomato Plant ◽  
Early Identification ◽  
Area Under The Curve ◽  
Tomato Plants ◽  
Novel Method ◽  
Optimum Model

Agriculture is one of the primary occupations in many countries. Tomatoes are grown by many farmers in countries where the water resource is available in abundance. Improper methods of cultivation and failure to identify the diseases when it is in the nascent stage results in the reduction of crop yield thus affecting the outcome of cultivation. This paper proposes a novel method of early identification of diseases in tomato plants by making use of convolutional neural networks (CNN) and image processing. Dataset from an open repository was considered for training and testing and the algorithm was capable of identifying nine different varieties of diseases that affect the tomato plant at its early stages. The images of tomato leaves were fed for identification through processing and classification. An optimum model was developed by analyzing various architectures of CNN including the VGG, ResNet, Inception, Xception, MobileNet and DenseNet. The performance of each of these architectures was compared and various metrics like the accuracy, loss, precision, recall and area under the curve (AUC) were analyzed.

scholarly journals Integrating Traditional Machine Learning and Neural Networks for Image Processing

2021 ◽  
Author(s):  
Nikita Laptev ◽  
Vladislav Laptev ◽  
Olga Gerget ◽  
Dmitriy Kolpashchikov
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Neural Network Models ◽  
Fire Source ◽  
Artificial Neural

The article describes a feasibility study to assess the use of neural networks and traditional machine learning algorithms to solve various problems including image processing. A brief description of some algorithms of traditional machine learning, as well as anautomated service for choosing the best method for a specific task, is given. The authors also describe the features of artificial neural networks and the most popular places for theirapplication. An algorithm for solving the problem of detecting fire hazardous objects andlocalizing a fire source in a forest using video sequence frames is presented. The article compares the characteristics of artificial neural network models according to the followingcriteria: underlying architecture, the number of analyzed frames, the size of the input image, the transfer learning model used as a feature vector composing network. Acomparative analysis of traditional machine learning algorithms and neural networks withlong short-term memory in the problem of classification of forest fire hazards is made. A solution to localization of the source of fire based on clustering is described. A hybrid algorithm for finding a fire source in a forest is developed and illustrated.

scholarly journals A primer on deep learning and convolutional neural networks for clinicians

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lara Lloret Iglesias ◽  
Pablo Sanz Bellón ◽  
Amaia Pérez del Barrio ◽  
Pablo Menéndez Fernández-Miranda ◽  
David Rodríguez González ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Capabilities ◽  
Computer Vision Applications ◽  
Fully Connected ◽  
Imaging Perspective ◽  
Adaptive Momentum

AbstractDeep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the machine learning field, such as adaptive momentum algorithms or dropout regularization, have dramatically improved the convolutional neural networks predicting ability, outperforming that of conventional fully connected neural networks. This work summarizes, in an intended didactic way, the main aspects of these cutting-edge techniques from a medical imaging perspective.

scholarly journals Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

2018 ◽  
Vol 5 (5) ◽  
pp. 939-945 ◽  
Author(s):  
Grace X. Gu ◽  
Chun-Teh Chen ◽  
Deon J. Richmond ◽  
Markus J. Buehler
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Additive Manufacturing ◽  
Convolutional Neural Networks ◽  
New Approach ◽  
Hierarchical Materials

A new approach to design hierarchical materials using convolutional neural networks is proposed and validated through additive manufacturing and testing.

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